class_docstr: Computes Binary Cross Entropy (BCE) loss. The BCELoss class expects output from Sigmoid function.
code: def __init__(
      self, reduction="sum", alpha=1.0, weight=torch.tensor([0.5, 0.5])
  ):
      super().__init__()
      self.reduction = reduction
      self.loss_weight = weight
      self.loss_f = torch.nn.BCELoss(
          weight=self.loss_weight, reduction=self.reduction
      )
  
file_path: NeMo/nemo/collections/asr/losses/bce_loss.py
imports: import torch
  from nemo.core.classes import Loss, Typing, typecheck
  from nemo.core.neural_types import LabelsType, LengthsType, LossType, NeuralType, ProbsType
label_desc: Use image node_id 3 to create a new BCELoss object from inherited base classes: Loss, Typing with example: obj = BCELoss(reduction, alpha, weight)